Educational Data Mining Overview Ryan S.J.d. Baker PSLC Summer School 2010.
Data Mining Presented By: Sean T. Ryan
Transcript of Data Mining Presented By: Sean T. Ryan
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Data Mining
Presented By:Sean T. Ryan
April 23, 2001CSMN 601
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Agenda Introduction Data Mining Techniques Technology Results Conclusion
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Challenges Faced by Businesses
Understanding CustomersCharacteristics & Profiles
Preferences
Know what they want to buy
Identifying attracting and returning profitable customers
Identifying opportunities
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Customer Data
Sales ForceInformation
Market Information
OrderEntry
CustomerService
Inventory
MarketingDatabase
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Data Integration => Mining
Sales Force information ->
Market information ->
Order Entry ->
Customer Service ->
Inventory ->
Marketing ->
DatabasDatabasee
Data Mining
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Definition:
Data mining is used to discover [hidden] patterns and relationships in your data in order to help you make better business decisions.
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Data Mining Introduction Data Mining Techniques Technology Results Conclusion
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Data Mining Techniques Include:
Sequences Associations Predictions Clustering Classification
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Sequences
One event leading toA latter event
Example: Customer purchases
A rugCustomer purchases
curtains
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Associations
One event is correlatedto another event
Example:
Beer purchasers
Will purchase peanuts aCertain percentage of the time
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Predictions
Discovering patterns in data that can lead to
predictions about the future
Examples:
Anticipate loan defaults& fraudulent behavior
Recommend products & service Offerings that match customer needs
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Identifying the Strategic Value of Customers
Valuable Growable 3rd Tier 4th Tier Nth Tier
Actual Value Strategic Value Data Mining Strategic Costs
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Clustering
Finding and visualizing groups of facts not previously known
Example:
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Who is likely to Buy a Buick?
Entire Population
Age<45Age>45
FemaleMale
Parents ownedBuick
Parents ownedForeign
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Classification
Classification by recognition of patterns
Example:
Detailed Profile of customer
Personalization
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Data Mining Introduction Data Mining Techniques Technology Results Conclusion
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Accrue Decision Series
KnowledgeSTUDIOKnowledgeSEEKER
Data Junction Integration Studio
Data Mining Software
SuperQuery.
BusinessMiner
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The Data Mining Group The data mining group is an
independent vendor group which develops data mining standards
PMML
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Data Mining Introduction Data Mining Techniques Technology Results Conclusion
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Florida Hospital improves operations with DB2 Intelligent Miner
"By using DB2 Universal Database and DB2 Intelligent Miner for Data, not only are we operating more efficiently, but we're getting people out of the hospital faster and back to their normal, healthy lives."
--Alex Veletsos, Director of Information Systems, Florida Hospital
Potential savings of $1.5 million per year
DM tools Investigate Patterns of care given by individual physicians & the total charges they generated
Standardized approaches to specific diagnoses
Efficiency in Accounts Payable
Establishment of training programs targeted to increase Medicare and insurance reimbursement
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The Human Genome Project
International research program designed to construct detailed genetic and physical maps of the human genome, to determine the complete sequence of DNA
Human Genome Project helps child birth defects
Professor Peter Scambler, Institute of Child Health in London
Utilize data mining applications to detect ‘bad’ genes that may play a role in certain birth defects
Information is deposited into public databanks accessible to other researchers, physicians, and drug developers
Control or alleviate symptoms
Determination of prognosis
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National Basketball Association
NBA coaches score big with IBM data mining application.
"By helping us make better decisions, Advanced Scout is playing a huge role in establishing incredible fan support and loyalty--that means millions of dollars in gate traffic, television sales and licensing."
--Tom Sterner, Assistant Coach, Orlando Magic
Optimizes Line ups
Real time statistical evaluations
Organizes & reveals patterns of a vast array of statistics
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Data Mining Introduction Data Mining Techniques Technology Results Conclusion
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In 2001, Do You Expect The Data Mining Industry To:
0 50 100 150 200
Have No Idea
Decline Significantly
Decline Slightly
Stay the Same
Grow Slightly
Grow Significantly
334 Votes Total
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Data Mining
Hidden Patterns Various Solutions Wide Variety of products Utilize Customer Data Know your Business needs Results!
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Questions